Subjects

Individual runs

First run

run1_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                                  family = binomial, data = run1_data)
##            term   estimate  std.error  statistic       p.value
## 1   (Intercept) -2.3535248 0.07394484 -31.828115 2.644027e-222
## 2        mask_c  0.1141508 0.10022396   1.138957  2.547212e-01
## 3        feat_c  0.3238523 0.10022443   3.231271  1.232410e-03
## 4 mask_c:feat_c  0.4674695 0.20045201   2.332077  1.969664e-02

Second run

run2_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                                  family = binomial, data = run2_data)
##            term    estimate  std.error   statistic       p.value
## 1   (Intercept) -2.58289325 0.07711001 -33.4962132 5.472142e-246
## 2        mask_c  0.07250391 0.10333206   0.7016594  4.828916e-01
## 3        feat_c  0.35802662 0.10333470   3.4647279  5.307682e-04
## 4 mask_c:feat_c -0.19824321 0.20665055  -0.9593161  3.373995e-01

Third run

run3_feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                                  family = binomial, data = run3_data)
##            term    estimate std.error   statistic       p.value
## 1   (Intercept) -2.60639763 0.1039194 -25.0809650 8.024030e-139
## 2        mask_c  0.03532078 0.1052895   0.3354634  7.372756e-01
## 3        feat_c  0.22769008 0.1052929   2.1624440  3.058397e-02
## 4 mask_c:feat_c  0.04856399 0.2106582   0.2305345  8.176765e-01

Overall

feat_type_error_mod <- glmer(is_error ~ mask_c * feat_c + (1|subj_id),
                             family = binomial, data = question_first)
##            term    estimate  std.error   statistic      p.value
## 1   (Intercept) -2.51772314 0.05241769 -48.0319405 0.000000e+00
## 2        mask_c  0.07415157 0.05923031   1.2519193 2.105993e-01
## 3        feat_c  0.30626015 0.05923297   5.1704338 2.335511e-07
## 4 mask_c:feat_c  0.11583961 0.11848037   0.9777114 3.282171e-01